CT-CFAR A Robust CFAR Detector Based on CLEAN and Truncated Statistics in Sidelobe-Contaminated Environments

TL;DR

This paper introduces a robust CFAR detection algorithm that uses CLEAN and truncated statistics to improve target detection accuracy in environments contaminated by sidelobes and abnormal interferences, without prior knowledge of anomalies.

Contribution

The paper presents a novel CFAR detector combining CLEAN, truncated statistics, and learnable sidelobe information to enhance robustness and accuracy in complex radar environments.

Findings

Achieves high-precision detection without prior knowledge of anomalies.

Outperforms existing CFAR algorithms in complex, sidelobe-contaminated scenarios.

Demonstrates superior detection performance and efficiency in simulations and real data.

Abstract

This paper proposes a constant false alarm rate (CFAR) target detection algorithm based on the CLEAN concept and truncated statistics to mitigate the non-homogeneity of reference samples caused by sidelobe contamination and other abnormal interferences within the reference window. The proposed algorithm employs truncated statistics to separate target and noise components in the radar echo power spectrum, thereby restoring the homogeneity assumption of the reference window. In addition, learnable historical sidelobe information is introduced to enhance the robustness and environmental adaptability of the detection process. Furthermore, based on multichannel echo data, a target reconstruction model that combines the Candan algorithm with least-squares estimation is established, incorporating the CLEAN concept to suppress sidelobe interference. Monte Carlo simulations and real-world measurement experiments demonstrate that the proposed CT-CFAR algorithm achieves high-precision target detection without requiring prior knowledge of abnormal samples. Compared with various CFAR algorithms, the proposed approach overcomes the limitations of the reference window, accurately estimates the noise spectrum, and exhibits superior detection performance and computational efficiency in complex scenarios affected by sidelobe contamination.